def __init__(
self,
problem: Problem,
population_size: int,
neighborhood: Neighborhood,
archive: BoundedArchive,
mutation: Mutation,
crossover: Crossover,
selection: Selection = BinaryTournamentSelection(
MultiComparator([
FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()
])),
termination_criterion: TerminationCriterion = store.
default_termination_criteria,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = store.default_comparator,
):
"""
MOCEll implementation as described in:
:param problem: The problem to solve.
:param population_size: Size of the population.
:param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
:param crossover: Crossover operator (see :py:mod:`jmetal.operator.crossover`).
:param selection: Selection operator (see :py:mod:`jmetal.operator.selection`).
"""
super(MOCell, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=1,
mutation=mutation,
crossover=crossover,
selection=selection,
termination_criterion=termination_criterion,
population_evaluator=population_evaluator,
population_generator=population_generator,
)
self.dominance_comparator = dominance_comparator
self.neighborhood = neighborhood
self.archive = archive
self.current_individual = 0
self.current_neighbors = []
self.comparator = MultiComparator([
FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()
])
def __init__(self,
reference_directions,
problem: Problem,
mutation: Mutation,
crossover: Crossover,
population_size: int = None,
selection: Selection = BinaryTournamentSelection(
MultiComparator([FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()])),
termination_criterion: TerminationCriterion = store.default_termination_criteria,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = store.default_comparator):
self.reference_directions = reference_directions.compute()
if not population_size:
population_size = len(self.reference_directions)
if self.reference_directions.shape[1] != problem.number_of_objectives:
raise Exception('Dimensionality of reference points must be equal to the number of objectives')
super(NSGAIII, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=population_size,
mutation=mutation,
crossover=crossover,
selection=selection,
termination_criterion=termination_criterion,
population_evaluator=population_evaluator,
population_generator=population_generator,
dominance_comparator=dominance_comparator
)
self.extreme_points = None
self.ideal_point = np.full(self.problem.number_of_objectives, np.inf)
self.worst_point = np.full(self.problem.number_of_objectives, -np.inf)
def __init__(
self,
problem: DynamicProblem[S],
population_size: int,
offspring_population_size: int,
mutation: Mutation,
crossover: Crossover,
selection: Selection = BinaryTournamentSelection(
MultiComparator([
FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()
])),
termination_criterion: TerminationCriterion = store.
default_termination_criteria,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: DominanceComparator = DominanceComparator()):
super(DynamicNSGAII, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=offspring_population_size,
mutation=mutation,
crossover=crossover,
selection=selection,
population_evaluator=population_evaluator,
population_generator=population_generator,
termination_criterion=termination_criterion,
dominance_comparator=dominance_comparator)
self.completed_iterations = 0
self.start_computing_time = 0
self.total_computing_time = 0
def __init__(
self,
problem: Problem,
population_size: int,
mutation: Mutation,
crossover: Crossover,
number_of_cores: int,
client,
selection: Selection = BinaryTournamentSelection(
MultiComparator([
FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()
])),
termination_criterion: TerminationCriterion = store.
default_termination_criteria,
dominance_comparator: DominanceComparator = DominanceComparator()):
super(DistributedNSGAII, self).__init__()
self.problem = problem
self.population_size = population_size
self.mutation_operator = mutation
self.crossover_operator = crossover
self.selection_operator = selection
self.dominance_comparator = dominance_comparator
self.termination_criterion = termination_criterion
self.observable.register(termination_criterion)
self.number_of_cores = number_of_cores
self.client = client
def __init__(self,
problem: Problem,
population_size: int,
offspring_population_size: int,
mutation: Mutation,
crossover: Crossover,
selection: Selection = BinaryTournamentSelection(
MultiComparator([
FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()
])),
termination_criterion: TerminationCriterion = store.
default_termination_criteria,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = store.default_comparator,
target_value_threshold: List[float] = None,
target_pattern: List[int] = None):
"""
NSGA-II implementation as described in
* K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," in IEEE Transactions on Evolutionary Computation,
vol. 6, no. 2, pp. 182-197, Apr 2002. doi: 10.1109/4235.996017
NSGA-II is a genetic algorithm (GA), i.e. it belongs to the evolutionary algorithms (EAs)
family. The implementation of NSGA-II provided in jMetalPy follows the evolutionary
algorithm template described in the algorithm module (:py:mod:`jmetal.core.algorithm`).
.. note:: A steady-state version of this algorithm can be run by setting the offspring size to 1.
:param problem: The problem to solve.
:param population_size: Size of the population.
:param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
:param crossover: Crossover operator (see :py:mod:`jmetal.operator.crossover`).
:param selection: Selection operator (see :py:mod:`jmetal.operator.selection`).
"""
super(NSGAII, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=offspring_population_size,
mutation=mutation,
crossover=crossover,
selection=selection,
termination_criterion=termination_criterion,
population_evaluator=population_evaluator,
population_generator=population_generator)
self.dominance_comparator = dominance_comparator
self.generation = 0
self.target_pattern = target_pattern
self.problem_solved = False
self.target_value_threshold = target_value_threshold
self.file_pareto_front = os.getcwd() + '/' + str(
time.strftime("%Y_%m_%d")) + '_PARETO_'
if not os.path.exists(self.file_pareto_front):
os.mkdir(self.file_pareto_front)
def __init__(self,
problem: MultiTransfer.MultiTransferProblem,
population_size: int,
offspring_population_size: int,
mutation: Mutation,
crossover: Crossover,
selection: Selection = BinaryTournamentSelection(
MultiComparator([FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()])),
termination_criterion: TerminationCriterion = store.default_termination_criteria,
population_generator: Generator = store.default_generator,
population_evaluator: Evaluator = store.default_evaluator,
dominance_comparator: Comparator = store.default_comparator):
"""
NSGA-II implementation as described in
* K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," in IEEE Transactions on Evolutionary Computation,
vol. 6, no. 2, pp. 182-197, Apr 2002. doi: 10.1109/4235.996017
NSGA-II is a genetic algorithm (GA), i.e. it belongs to the evolutionary algorithms (EAs)
family. The implementation of NSGA-II provided in jMetalPy follows the evolutionary
algorithm template described in the algorithm module (:py:mod:`jmetal.core.algorithm`).
.. note:: A steady-state version of this algorithm can be run by setting the offspring size to 1.
:param problem: The problem to solve.
:param population_size: Size of the population.
:param mutation: Mutation operator (see :py:mod:`jmetal.operator.mutation`).
:param crossover: Crossover operator (see :py:mod:`jmetal.operator.crossover`).
:param selection: Selection operator (see :py:mod:`jmetal.operator.selection`).
"""
super(NSGAII_MEDA, self).__init__(
problem=problem,
population_size=population_size,
offspring_population_size=offspring_population_size,
mutation=mutation,
crossover=crossover,
selection=selection,
termination_criterion=termination_criterion,
population_evaluator=population_evaluator,
population_generator=population_generator
)
self.dominance_comparator = dominance_comparator
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = TSP(instance='../../resources/TSP_instances/kroA100.tsp')
print('Cities: ', problem.number_of_variables)
algorithm = GeneticAlgorithm(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PermutationSwapMutation(1.0 / problem.number_of_variables),
crossover=PMXCrossover(0.8),
selection=BinaryTournamentSelection(
MultiComparator([
FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()
])),
termination_criterion=StoppingByEvaluations(max=2500000))
algorithm.observable.register(observer=PrintObjectivesObserver(1000))
algorithm.run()
result = algorithm.get_result()
print('Algorithm: {}'.format(algorithm.get_name()))
print('Problem: {}'.format(problem.get_name()))
print('Solution: {}'.format(result.variables))
print('Fitness: {}'.format(result.objectives[0]))
print('Computing time: {}'.format(algorithm.total_computing_time))
def construct(self, hyperparameters: Mapping, scenario: Mapping,
warm_startup_info: Mapping) -> None:
# Constructing meta-heuristics initialization arguments (components + simple hyperparameters)
init_args = dict(copy(hyperparameters))
if "crossover_type" in init_args:
import jmetal.operator.crossover as crossover
crossover_class = JMetalPyWrapper._get_class_from_module(
name=init_args.pop("crossover_type"), module=crossover)
init_args["crossover"] = crossover_class(
init_args.pop("crossover_probability"))
if "mutation_type" in init_args:
import jmetal.operator.mutation as mutation
mutation_class = JMetalPyWrapper._get_class_from_module(
name=init_args.pop("mutation_type"), module=mutation)
init_args["mutation"] = mutation_class(
init_args.pop("mutation_probability"))
if "selection_type" in init_args:
selection_type = init_args.pop('selection_type')
if selection_type == 'ReuletteWheelSelection':
from jmetal.util.comparator import MultiComparator
from jmetal.util.density_estimator import CrowdingDistance
from jmetal.util.ranking import FastNonDominatedRanking
init_args["selection"] = MultiComparator([
FastNonDominatedRanking.get_comparator(),
CrowdingDistance.get_comparator()
])
else:
import jmetal.operator.selection as selection
selection_class = JMetalPyWrapper._get_class_from_module(
name=selection_type, module=selection)
init_args["selection"] = selection_class()
# Add all non-component Metaheuristic parameters
if "offspring_population_size" in init_args:
offsp_population = init_args.pop("offspring_population_size")
# offspring_population_size should be even
offsp_population += offsp_population % 2
init_args['offspring_population_size'] = offsp_population
# elitist should be bool
if "elitist" in init_args:
init_args['elitist'] = True if init_args.pop(
'elitist') == "True" else False
if "population_size" in init_args:
init_args["population_size"] = init_args.pop("population_size")
termination_class = JMetalPyWrapper._get_class_from_module(
name=scenario["Budget"]["Type"], module=termination)
init_args["termination_criterion"] = termination_class(
scenario["Budget"]["Amount"])
# making dict hashable enables using LRU cache
problem_init_params = HashableDict(
scenario['problem_initialization_parameters'])
problem = JMetalPyWrapper._get_problem(
problem_name=scenario['Problem'], init_params=problem_init_params)
init_args["problem"] = problem
# Attach initial solutions.
self.load_initial_solutions(warm_startup_info, problem)
llh_name = init_args.pop("low level heuristic")
if llh_name == "jMetalPy.SimulatedAnnealing":
init_args["solution_generator"] = self._solution_generator
else:
init_args['population_generator'] = self._solution_generator
# Instantiate Metaheuristic object
self._llh_algorithm = JMetalPyWrapper._get_algorithm_class(llh_name)(
**init_args)
